Interpreting Graphs
Overview
Expect to see a number of questions on the SAT that deal with interpreting information presented graphically. In this section, we will review the following aspects of this topic:
 Drawing Conclusions from Graphs: Categorical Data
 Drawing Conclusions from Graphs: Continuous Data
Let’s look at each of these in more detail:
Drawing Conclusions from Graphs: Categorical Data
Different data types result in different types of graphs. Categorical data includes nonnumerical data. For example, here is a data table that shows fruit preferences for students in a class. Column 1 of the data set consists of the categories (the fruit). Column 2 of the data set is numerical. This is a categorical data set.












Here is the bar graph of that data:
One type of graph that you are likely to see is a line graph. What is a line graph? It usually starts with a data table. This table tracks the average number of cars sold each day of the week at a car dealership. Note that column 1 is categorical, the days of the week, while column 2 is numerical.
















Line graphs are useful for showing a trend over time. Here is a graph of the data.
The horizontal axis includes the days of the week, which corresponds to column 1 of the data table. The vertical axis is the number of cars sold, which corresponds to the other data column.
From this graph you can conclude the following:
 More cars are sold on Saturday and Sunday.
 The least number of cars are sold on Monday and Tuesday.
Expect to see a line graph with one or more questions that involve analyzing the graph.

Drawing Conclusions from Graphs: Continuous Data
Another data type you should be familiar with is continuous data. You’ll often see bivariate data (twovariable data) that consists of a set of measurements. Here is an example.
This scatterplot shows the relationship between the number of hours studied and the results on a test for a number of students. What can you conclude from this graph?
The horizontal axis tracks the number of hours studied. The vertical axis represents the test score. A scatterplot like this shows a functional relationship between the two variables. This scatterplot shows a linear relationship and the green line is known as the line of best fit.
From this graph we can conclude that as the number of hours increased, so did the test score.

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